Soil quality is indicated by the interaction of physical, chemical and biological soil properties. The importance of physical properties, for example soil structure, lies in the fact that they enhance chemical and biological soil functions. Consequently, periodic assessment of structural quality is an important aspect of soil quality management. Quantitative soil properties can be used as indirect indicator parameters for soil structural changes. However, measuring these properties is not applicable, especially for smallholder farmers who cannot afford to pay for laboratory tests. This study contributes to the validation of visual field assessments by comparing the performance of such methods on ‘tropical’ soils. The study was conducted across two regions with contrasting soil types and land use, in the sub-humid with clay Nitiosl and semi-arid with sandy loam Cambisol locations of Kenya. At both locations, visual methods were tested on soils under cropland and under natural forests (NF). Under the cropland, evaluation sites were selected from researcher and farmer managed sites. Visual scores from visual soil assessment (VSA), visual evaluation of soil structure (VESS) and visual evaluation of soil structure using the core (coreVESS) were correlated with soil physical and chemical properties measured in the laboratory. Under the clay Nitisol, absolute values of Pearson r between VSA scores and laboratory measured soil properties ranged from 0.84 to 0.54, for VESS, they varied between 0.75 and 0.37 while for coreVESS, they ranged from 0.84 to 0.60. For the sandy loam Cambisol, absolute Pearson r values between laboratory measured soil properties and VSA scores ranged from 0.83 to 0.29, the r values were between 0.88 and 0.45 for VESS and between 0.81 and 0.40 for coreVESS. From the obtained correlations, we concluded that the visual methods tested are capable of distinguishing structural quality due to different land use and are therefore suitable for assessing soil structural quality of tropical soils in Africa. Management thresholds were determined using bulk density (BD). The target value for good soil quality (Sq<2) for the Kibugu Nitisol was BD = 0.0012*SOC+0.6476 (r = 0.71; SOC is soil organic carbon), while the trigger and remediation values were 0.93 Mg m⁻³ (Sq = 2) and 0.99 Mg m⁻³ (Sq = 3), respectively. In the absence of SOC data, the target mean BD for Sq<2 is 0.79 Mg m⁻³. For Machang’a Cambisol, the target, trigger and remediation values were 1.48 Mg m⁻³ (Sq = 2), 1.56 Mg m⁻³ (Sq = 3) and 1.64 Mg m⁻³ (Sq = 4), respectively.

土壤质量通过物理、化学和生物土壤特性的相互作用来指示。物理特性（例如土壤结构）的重要性在于它们增强了土壤的化学和生物功能。因此，定期评估结构质量是土壤质量管理的一个重要方面。定量土壤性质可用作土壤结构变化的间接指标参数。然而，测量这些特性并不适用，特别是对于支付不起实验室检测费用的小农。本研究通过比较此类方法在“热带”土壤上的性能，有助于验证视野评估。该研究在两个土壤类型和土地利用不同的地区进行，在半湿润的粘土 Nitiosl 和半干旱的砂壤土 Cambisol 位置的肯尼亚。在这两个地点，在农田和天然林 (NF) 下的土壤上测试了视觉方法。在农田下，评估地点是从研究人员和农民管理的地点中选择的。视觉土壤评估 (VSA)、土壤结构视觉评估 (VESS) 和使用核心的土壤结构视觉评估 (coreVESS) 的视觉评分与实验室测量的土壤物理和化学性质相关。在粘土 Nitisol 下，VSA 分数和实验室测量的土壤性质之间的 Pearson r 绝对值范围为 0.84 至 0.54，对于 VESS，它们在 0.75 和 0.37 之间变化，而对于核心 VESS，它们的范围从 0.84 到 0.60。对于砂质壤土 Cambisol，实验室测量的土壤特性和 VSA 分数之间的绝对 Pearson r 值介于 0.83 到 0.29 之间，VESS 的 r 值介于 0.88 和 0.45 之间，coreVESS 的 r 值介于 0.81 和 0.40 之间。根据获得的相关性，我们得出结论，所测试的视觉方法能够区分由于不同土地用途而导致的结构质量，因此适用于评估非洲热带土壤的土壤结构质量。使用体积密度 (BD) 确定管理阈值。Kibugu Nitisol 良好土壤质量（Sq<2）的目标值为 BD = 0.0012*SOC+0.6476（r = 0.71；SOC 为土壤有机碳），而触发值和修复值为 0.93 Mg m 从获得的相关性中，我们得出结论，所测试的视觉方法能够区分由于不同土地利用而导致的结构质量，因此适用于评估非洲热带土壤的土壤结构质量。使用体积密度 (BD) 确定管理阈值。Kibugu Nitisol 良好土壤质量（Sq<2）的目标值为 BD = 0.0012*SOC+0.6476（r = 0.71；SOC 为土壤有机碳），而触发值和修复值为 0.93 Mg m 从获得的相关性中，我们得出结论，所测试的视觉方法能够区分由于不同土地利用而导致的结构质量，因此适用于评估非洲热带土壤的土壤结构质量。使用体积密度 (BD) 确定管理阈值。Kibugu Nitisol 良好土壤质量（Sq<2）的目标值为 BD = 0.0012*SOC+0.6476（r = 0.71；SOC 为土壤有机碳），而触发值和修复值为 0.93 Mg m^-3 (Sq = 2) 和 0.99 Mg m ^-3 (Sq = 3)。在没有 SOC 数据的情况下，Sq<2 的目标平均 BD 是 0.79 Mg m ^-3。对于 Machang'a Cambisol，目标值、触发值和修复值分别为 1.48 Mg m ^-3 (Sq = 2)、1.56 Mg m ^-3 (Sq = 3) 和 1.64 Mg m ^-3 (Sq = 4)。